Financial Reforms and Technical Efficiency in Indian Commercial Banking

34 Pages·2011·0.17 MB·English

Financial Reforms and Technical Efficiency in Indian
Commercial Banking: A Generalized Stochastic
Frontier Analysis
Aditi Bhattacharyya∗ Sudeshna Pal†
November 2011
Abstract
In this study we estimate technical efficiency of Indian commercial banks from 1989-
2009 using a multiple-output generalized stochastic production frontier, and analyze the ef-
fectsoffinancialsectorreformsonmeasuredefficiency. Thisgeneralizedtechniqueestimates
technicalefficiencyinthepresenceofmultipleoutputs,fillingagapintheexistingliterature.
Our results show that Indian commercial banks were operating with 64% efficiency on av-
erageduringthesampleperiodandthatefficiencydeclinedinbothpublicandprivatebanks
during most parts of the post-reform period. The capital adequacy ratios negatively influ-
enced efficiency while the number of branches had no significant effect on bank efficiency.
Financialsectorreforms,however,havehadmixedresultsontechnicalefficiency. Theinitial
phaseof reformhad positiveimpacton technical efficiencywhile the laterphases adversely
affected technical efficiency of commercial banks. Throughout the sample period, public
sectorbanksshowhigherefficiencylevelscomparedtoprivatesectorandforeignbanks.
JELCode: D24;G21;G28
Keywords: TechnicalEfficiency;GeneralizedStochasticProductionFrontier;
IndianCommercialBanks;FinancialReforms;Liberalization
∗Department of Economics and International Business, Sam Houston State University, Huntsville, TX 77341.
Ph: 936-294-4791. Email: [email protected]
†DepartmentofEconomicsandFinance,GeorgiaCollegeandStateUniversity,CBXNo.014,Milledgeville,GA
31061. Ph: 478-445-1240. Email: [email protected]
1 Introduction
ThegoalofthisstudyistoestimatetechnicalefficiencyofIndiancommercialbanksandexamine
the effects of financial sector reforms on the measured efficiency. The role of the financial sector
in promoting economic growth and development has been widely acknowledged in the litera-
ture (e.g. Schumpeter (1911); Goldsmith (1969); Mckinnon (1973); Shaw (1973); Fry (1978);
King and Levine(1993a, 1993b); Levine (2005)). Studies show that “financial repression” or the
existence of rigid government control on interest rate and lending policies of banks and other
financial institutions, under-lending, high uncertainty on returns on savings and investments and
misallocation of savings among competing users, among other things, stunts the development
of the financial sector and further hinders economic growth in both developed and developing
countries(e.g. Fry(1980,1997);Haber(2003);Barthetal.(2006);Abiadetal.(2008).
The financial sector in post-independence India had all the characteristics of financial
repression. Banks were nationalized and there was strong government control over the finan-
cial market. “The sector was characterized, inter alia, by administered interest rates, large pre-
emption of resources by the authorities and extensive micro-regulations directing the major por-
tionoftheflowoffundstoandfromfinancialintermediaries”(Mohan2004,p851). Theoutcome
was lack of competition, high intermediation costs and hence under-lending, corruption and bu-
reaucratic lethargy (e.g. Banerjee et al. (2004); Mohan (2005); Thomas (2005)). In 1991 the
Indian government launched wide-spread economic liberalization policies which also pervaded
the financial sector. Entry barriers were loosened making way for private and foreign banks,
reforms were initiated to improve “financial soundness” and bank efficiency targeting capital
adequacy requirements, stronger vigilance of the banking sector and several other legal and in-
stitutional factors (Ahluwalia (2002); Mohan (2005)). The banking sector reforms in India were
implementedintwophases,firstin1991-92followedbyasecondphasein1998.
Indiapresentsaninterestingcaseinthestudyofbankefficiencyowingtotheco-existence
ofalargenumberofgovernmentowned,privateandforeignbanksintheeconomy. India’srapid
economic growth also makes examination of the performance of the banking sector an attractive
subjectforresearch,especially,aftertheimplementationofwidespreadeconomicreforms.
2
Abankissaidtobetechnicallyinefficientiftheactualoutputislowerthanthemaximum
possible output level, given available resources. Common causes of such inefficiency includes
managerial error or co-ordination failure. The existing literature in this field uses mainly two
types of methods to measure technical efficiency of banks – Data Envelopment Analysis (DEA)
andStochasticFrontierAnalysis(SFA).
The DEA method uses linear programming techniques to measure efficiency of produc-
tion units that produce multiple outputs. Several studies use this approach to measure efficiency
of Indian banks (see Bhattacharyya, Lovell, and Sahay (1997); Sathye (2003); Das and Ghosh
(2006);Das,Nag,andRay(2005);KumarandGulati(2009)). However,thismethodfailstocap-
ture the effect of random shocks to the production system. On the other hand, the SFA method
posits two main causes for the deviation of actual output from the maximum possible output,
given the inputs. A part of this deviation is attributed to the symmetric random shocks to a
production system that are not under the control of a producer (e.g., uncertainty about the envi-
ronment, or input market conditions). The other part is attributed to factors such as managerial
error and coordination failures. The existing studies that use SFA to measure different types of
efficiencyofIndianbankseitherfocusonmeasuringcostandprofitefficiencyinmonetaryterms
to avoid the problem of considering multiple output (see Sensarma (2006), Zhao et al. (2010)),
or measure technical efficiency using a simple production model with one type of output only
(see Shanmugam and Das (2004)). In reality, banks produce many financial services (outputs)
using a given set of inputs, and none of the existing studies use the stochastic frontier approach
tomeasuretechnicalefficiencyofbanksinsuchaframework.
In this paper, we use the Generalized Stochastic Frontier Production Model that allows
for the possibility of multiple outputs in a production system, as introduced by Lo¨thgren(1997),
and measure time-varying technical efficiency of Indian banks. Further, we analyze effects of
financial reforms and other bank specific characteristics on the measured efficiency scores using
apaneldataspanningtheperiodfrom1989to2009.
Our study finds that the average technical efficiency of both public and private sector
commercial banks in India is low and declined during most parts of the period under considera-
tion,showinganimprovementonlytowardstheendofthesampleperiod. Capitaladequacyratio
negatively influenced technical efficiency of Indian commercial banks, particularly, the private
3
banks. Although the gradual process of financial reforms, as experienced by the country for last
two decades, shows some positive impact on technical efficiency of banks in the beginning, the
effectseemstodiedownthereafter.
OurfindingofadecliningtrendinefficiencylevelsofIndianbanksovertime,isinaccor-
dancewithastudybyDasandGhosh(2006),whichusesDEAtomeasuretechnicalefficiencyof
Indian banks over the period 1992-2002. Another study by Zhao, Casu, and Ferrari (2010) also
finds declining cost-efficiency in Indian banking over the period from 1992 to 2004. Our study
encompassesalongerperiodoftimethanDasandGhosh(2006)andZhaoet.al. (2010),anduses
a more general technique of efficiency measurements as compared to DEA and the traditional
single-outputSFA.
The following section briefly outlines the general structure of the Indian commercial
banks and the reform programs that were initiated from the early 1990s. Section 3 presents
details about the data used in this study and the empirical model specifications. The empirical
findingsarediscussedinsection4,andsection5concludes.
2 A Brief Overview of the Indian Banking Sector
India’s banking sector is characterized by public sector banks, private sector banks and foreign
banks. In the 1950s the financial sector in India had limited government control on interest
rates and low statutory pre-emption of funds resulting in unequal distribution and misallocation
of credit. This was not only defying the free market mechanism of credit allocation but also
depriving sectors that were in true need of credit (Das and Kumbhakar (2010)). In order to
ensure more equitable distribution of credit, the government tightened its control over banks’
credit allocation procedures and nationalized 20 major commercial banks between 1969-1980
(Das et al. (2005)). Consequently, administered interest rates, large pre-emption of resources by
the authorities and extensive micro-regulations directing the major portion of the flow of funds
to and from financial intermediaries, inter alia, characterized the Indian financial sector in the
1980s. Government controls and regulations also created strong entry barriers. In the absence of
competition,India’sfinancialsectorwasrenderedinefficientandnon-competitivecreatingsevere
creditconstraintsforothersectorsoftheeconomy(Mohan(2005),Thomas(2005)).
4
Banking sector reforms in India that were initiated in the early 1990s was a gradual and
steadyprocess. Onecanidentifytwodistinctphasesofthesereforms. Thefirstphasebeganwith
the formation of the Committee on the Financial System (The Narasimhan Committee) in 1991
andthesecondphaseofreformsinitiatedwiththeformationoftheBankingSectorReformsCom-
mittee(NarasimhanCommitteeII)in1998. Bothcommitteesrecommendedwidespreadreforms
forthebankingsystem,capitalmarketsandtheinsurancesector. Bankingsectorreformsincluded
variousliberalizationpolicies,suchasrelaxingcontrolsoninterestratesandthesanctionoflarge
loans by the Reserve Bank of India, and policies that promote competition, such as designing
liberal norms for entry of private and foreign banks and insurance companies, and allowing in-
flow of foreign direct investment in the financial sector. The reforms also included measures
to improve “financial soundness”, like capital adequacy requirements, stronger vigilance of the
bankingsectorandseveralinstitutionalandlegalmeasurestoimprovebankefficiency(Ahluwalia
(2002);Mohan(2005)).
These reforms resulted in the expansion of private and foreign banks in India while low-
ering the share of public sector banks’ assets in total bank assets. The share of public sector
banks’ assets in total assets declined from 92% in 1990-91 to 75% in 2003-04; at the same time
the share of private sector banks went up from 4% in 1990-91 to 19% in 2003-04. The fact that
the banking sector became more competitive following the reforms is shown by the reduction in
theten-firmconcentrationratioof92.86in1991-92to62.99in2004-05(Thomas(2005)).
3 Data and Econometric Model Specification
3.1 Data
Technical efficiency is measured using bank level data from the Prowess database obtained from
CenterforMonitoringtheIndianEconomy(CMIE).Prowesshasauditedfinancialdataonfinan-
cial sector companies of which we consider companies providing commercial banking services
only. The data also provides information on bank ownership, namely, public and private and
furthercategoriessuchasIndianprivatebanksandforeignprivatebanks. Ourdataconsistsofan
unbalancedpanelofupto103commercialbanksfrom1989-2009.
5
The common practice is to adopt either an intermediary approach or a production ap-
proachtomeasuretechnicalefficiencyofbanks. Thetwoapproachesusedifferentsetsofoutputs
and inputs. Based on data availability we follow the intermediary approach, under which banks
produce intermediation services like investments, loans and advances through the collection of
liabilitieslikedeposits. Thisapproachalsoincludesexpensesforhiringlaborandrentingcapital
asinputsofthebankingsystem. Henceweestimatetechnicalefficiencyusingbankleveldataon
threeinputvariables–compensationtoemployeesandcapitalexpenses(calculatedbyaggregat-
ing expenditures on power and fuel, indirect taxes, rent and lease, repairs, insurance premium,
printing,stationaryanddepreciation)anddepositsacceptedbycommercialbanks. Theoutputof
banks is defined as a mix of investments, and loans and advances. We describe the basic method
toconstructtheoutputmixfromthesetwooutputsinsection3.2.1
We measure financial reforms by constructing dummies for several time periods to take
into account the gradual nature of the liberalization process. The time dummies are 1993-1996,
1998-2001,2002-2005and2006-2007. Theempiricalmodelthatestimatestheeffectsofreforms
ontechnicalefficiencyofbanksalsocontrolsforbankspecificfactorssuchasnumberofbranches
andcapitaladequacyratio. Numberofbranchescontrolsformanagerialinefficienciesthatmight
set in when a bank has too many branches and negatively affect technical efficiency (Das and
Ghosh(2006)). Capitaladequacyratio(CAR)accountsforthedegreeofcapitalizationofbanks.
Bankswithahighcapitaladequacyratiocanbeconsideredsafe,decreasingtheircostofborrow-
ingandhenceincreasingefficiency(DasandGhosh(2006)). OntheotherhandahighCARmay
imply conservative behavior on the part of banks in terms of lending, leading to lower efficiency
(Bhattacharyyaetal.(1997)).
Both regression models use logarithmic values of dependent and independent variables
(except time trend and time dummies). Table 1 presents the summary statistics of the data used
inouranalysis. AppendixAprovidesadetaileddescriptionofthevariables.
1AppendixBpresentsthedetailedmethodofconstructingtheoutputmix.
6
3.2 Model Specification
We specify a panel data stochastic production frontier model for Indian commercial banks in a
generalized production frontier framework (Lo¨thgren(1997)), also known as the Ray Production
Frontier, as discussed in Appendix B. The Ray Production Frontier considers multiple outputs
producedbybanks,andconstructsanoutputmeasurebasedontheproductionpossibilityfrontier
representingtherelationshipamongtheoutputs. Thepolarco-ordinateanglesbetweentheoutput
vectors play an important role in determining the relationship between the output measure and
inputs. Let i = 1,...,N be the number of banks, t = 1,...,T be the number of time periods under
consideration, and p = 1,...,P be the number of outputs generated by every bank. Assuming
that the banking services are generated by a Cobb-Douglas function2, the generalized stochastic
productionmodel3 isgivenby
ln(cid:107)y (cid:107) = β +z(cid:48) β+v −u ,i = 1,...N;t = 1,...,T (3.2.1)
it 0 it it it
 
where(cid:107)yit(cid:107) = (cid:88)P (cid:16)yipt(cid:17)21/2,p = 1,...,P
p=1
yp = the pthoutput(inlogarithmicterms)producedbybank iattimet
it
β = thefrontierintercept
0
β = thevectoroftechnologyparameters
z = thevectorincluding d inputs, P−1polar-coordinates,timetrend,
it
andotherpossibleexogenousvariablesthatvarywithbankandtime
v = randomshocksthataffectserviceprovisionbythebanks;v i∼idN(0,σ2)
it it v
u = technicalinefficiencyofbank iattimet;u ≥ 0
it it
Allthevariablesusedinequation(3.2.1)areintheirnaturallogarithmicformsexceptforthetime
trend.
2Aflexibleformproductionfunctioncanbeusedaswell. However,thetechnicalefficiencyscores,ascalculated
usingCobb-DouglasandTrans-logarithmicproductionfunctions,aresimilarforourdata.
3AdetaileddiscussiononthegeneralizedstochasticproductionfrontierispresentedinAppendixB.
7
WeusetheBattesse-Coelli(1992)parameterizationofthetime-varyinginefficiencyterm
which is modeled as a truncated normal variable multiplied by a specific function of time. For-
mally,
(cid:8) (cid:2) (cid:3)(cid:9)
u = exp −η(t−T) u,t ∈ I(i);i = 1,...,N (3.2.2)
it i
(cid:16) (cid:17)
where,u = iid non-negativetruncationsof N µ,σ2
i u
η = unknownscalarparametercapturingthevariationintechnicalinefficiencywithtime
I(i) = setofT timeperiodsamongtheTperiodsinvolvedforwhichobservationsfor
i
theithbanksareobtained.
For η > 0 (or η < 0), banks tend to improve (or reduce) their level of technical efficiency
over time, and the technical efficiency level tends to remain constant over time for η = 0. The
maximumlikelihoodestimationof(3.2.1)and(3.2.2)isdiscussedinBattesseandCoelli(1992).
In order to estimate the effects of financial reforms on efficiency in Indian banks, we
further model the technical efficiency scores, using a fixed effects panel data regression model4
whichisgivenby
(cid:88)m
lnu = δ + δ q +ε (3.2.3)
it 0 j jit it
j=1
where, q is a vector of reform dummies and other bank and time specific explanatory variables
it
(inlogarithmicterms)associatedwithtechnicalefficiency.
Toestimatetechnicalefficiencyscoresofeachbankateverytimeperiod,wefirstestimate
the generalized stochastic production frontier, as described in equation (3.2.1) where the output
norm(cid:107)y (cid:107)isderivedusingtwooutputs,viz. investmentandloansandadvances(ladv),following
it
DasandGhosh(2006). Usingthevaluesforthesetwooutputs,wemeasuretheoutputnormas
(cid:16) (cid:17)
1/2
(cid:107)y (cid:107) = investment2 +ladv2 (3.2.4)
it it it
4Wemodeltechnicalefficiencyasafixedeffectsmodel,allowingforthepossiblecorrelationbetweentherandom
shocks affecting technical efficiency of banks and bank specific factors that influence efficiency. However, the
estimationresultsfor(3.2.3)usingarandomeffectsmodelandafixedeffectsmodelareverysimilar.
8
Inputsusedbybanksintheproductionofserviceslikeinvestmentandloansandadvancesarethe
deposits accepted by them, capital, and employees. Hence, we include the value of deposits ac-
ceptedbycommercialbanks(deposits),compensationtoemployees(labor)andcapitalexpenses
(capital) as inputs in the production model. We use the polar-coordinate angle θ (radian) cor-
inv
respondingtotheoutputvector“investment”asanotherdeterminantoftheoutputnorm. Finally,
we capture the effect of technological changes on the production frontier by incorporating the
timetrend(yearid)intheproductionmodelandestimatethefrontiermodelas
ln(cid:107)y (cid:107) = β +β ln(deposits)+β ln(labor)+β ln(capital)+β ln(θ (rad)) (3.2.5)
it 0 1 2 3 4 inv
+β (yearid)+v −u
5 it it
Thetime-varyingtechnicalefficiencyscoresarethenestimatedusing(3.2.2).
Inthenextstage,weanalyzetheeffectsoffinancialreformsontechnicalefficiencyscores.
Since the reforms were implemented in phases, we construct several dummies for different time
intervals. The dummies are described below5. We also control for certain bank specific factors,
namely,numberofbranchesandcapitaladequacyratiowhichmayalsohaveinfluencedtechnical
efficiencyofbanks.
lnu = δ +δ ln(branches)+δ ln(CAR)+δ (1993−1996) (3.2.6)
it 0 1 2 3
+δ (1998−2001)+δ (2002−2005)+δ (2006−2009)+ε
4 5 6 it
where,
ln(u ) = ln(technicalefficiency)
it
ln(branches) = ln(no. ofbranches)
ln(CAR) = ln(capitaladequacyratio)
1993−1996 = 1if1992 < year < 1997,0otherwise
1998−2001 = 1if1997 < year < 2002,0otherwise
2002−2005 = 1if2001 < year < 2006,0otherwise
2006−2009 = 1if2005 < year,0otherwise
5Sincemajorphasesofreformswereintroducedin1992and1997,wemeasuretheeffectsofreformspost1992
and1997andconstructourdummiesaccordingly.
9
Wealsoestimatetheabovemodelaccordingtoownershipgroupsandanalyzeimpactsofexoge-
nous variables on the technical efficiency scores, separately for each category. For this purpose,
we divide the data as banks owned by the government sector or public sector banks and private
sector(foreignanddomestic)banks.
4 Empirical Results
4.1 Results of Efficiency Analysis
Estimation results for the generalized production frontier model (equations 3.2.5 and 3.2.2) are
presented in Table 2. All three inputs have positive input elasticities. This in turn, also verifies
the positive input monotonicity of the ray production frontier. We also find that the sum of
inputelasticities(βˆ +βˆ +βˆ )is0.90,indicatingthatserviceprovisionbytheIndianCommercial
1 2 3
Banksfollowsadecreasingreturnstoscale. TheWaldChi-squaretestforthenullhypothesisthat
the production model exhibits constant returns to scale is rejected at 1% significance level. The
testresultsalsoshowthatthesumoftheinputelasticitiesissignificantlylessthanunity,implying
thattheproductionfunctionexhibitsdecreasingreturnstoscale.
The significantly positive coefficient of the polar-coordinate angle indicates that for a
givenlevelofinputs,thefrontieroutputnormdependsontheoutputmix6. Duetotheinefficiency
effects,theobservedoutputnormisobtainedbyacontractionofthefrontiernorm. Thecoefficient
estimateof θ (rad)implies thata decreasein investmentsonthe productionfrontier results ina
inv
morethanproportionateincreaseinloansandadvances. Thisresultisinaccordancewiththeidea
that making investments in assisted companies, shares, mutual funds, and government securities
aremoreresourceconsumingthanmakingloansandadvances.
The technical efficiency scores are calculated using the estimation results from equations
(3.2.5) and (3.2.2) and presented in Table 3.7 We find that on average, the efficiency scores of
Indiancommercialbanksare63.73%duringtheperiod1989-2009.
6Theoutputmixrepresentsthedifferentcombinationsofoutputsproducedwithgivenresourcesandtechnology.
7Sinceweuse1431observationsonbanksandcalculatetechnicalefficiencyofeachbankateverytimeperiod,
we do not report 1431 such scores. Instead we summarize the estimated technical efficiency scores in Table 3.
AppendixDpresentstechnicalefficiencyscoresandrankofeachbankinourdataset.
10